Robust Signal-to-Noise Ratio Estimation in Non-Gaussian Noise Channel

Signal-to-noise ratio (SNR) estimation available in the literature are designed based on the assumption of Gaussian noise models. These estimators may produce misleading results when the distribution of the noise deviates from Gaussian. This paper investigates the performance of existing SNR estimators in an additive non-Gaussian noise channel based on a Gaussian mixture model. An expectation–maximization (EM) based approach is proposed for optimum SNR estimation in the non-Gaussian noise channel. In addition, the Cramer–Rao bound is derived and used as a benchmark to assess the performance of the SNR estimators. Simulation results confirm the optimality and robustness of the proposed EM-based estimator in Gaussian and non-Gaussian noise channels.